A Memory-Based Colonization Scheme for Particle Swarm Optimization

Abstract

A novel memory-based particle swarm optimization algorithm employing externally implemented global (shared) and particle-based (local) memories and a colonization approach similar to artificial immune system algorithms is presented. At any iteration, particle-based memories keep a number of previously best performing personal positions for each particle and the global memory keeps a number of globally best positions found so far. A set of velocities is computed for each particle using each of the personal best positions within its local memory and a number of randomly selected positions from the global memory. This way, a colony of new positions is obtained for each particle and the one with the best fitness is selected and put within the new swarm. Global and local memories are also updated using the solutions within each colony. This new memory-based strategy is used for the solution of problems within the CEC2005 test suit. Experimental evaluations demonstrated that the proposed strategy outperformed the conventional and other known memory-based PSO algorithms for all problem instances.